Course Identification
Projects in data science
Lecturers and Teaching Assistants
Dr. Yaron Antebi, Dr. Leeat Yankielowicz-Keren
Course Schedule and Location
Second Semester
Wednesday, 14:00 - 16:00, Science Teaching Lab 1
18/03/2026
Field of Study, Course Type and Credit Points
Life Sciences: Seminar; Elective; Regular; 3.00 points
Life Sciences (Computational and Systems Biology Track): Seminar; Obligatory; 3.00 points
Comments
First-class will be held on the first Wednesday of the semester from 14-16.
Subsequent meetings will be set individually by each lecturer. 2-3 hrs each.
Prerequisites
Previous knowledge in programming (in R/Matlab/Python) is required for this course.
Attendance and participation
Estimated Weekly Independent Workload (in hours)
Syllabus
The course is aimed to introduce the students to aspects of data analysis through working on specific projects in small groups (2-3 students). Each group will work closely with a lecturer in the course (PI from a computational biology group) to design and implement a computational analysis to address a biological question related to their lab. Each group will work independently, with weekly supervision of the lecturer, and at the end of the course they will present their work in a seminar and in writing.
Timeline:
Week 1:
In the first meeting each lecturer will present a small project from his lab, and students will then be divided to groups of 2-3 (considering their preferences). Each of the groups will then meet with the designated lecturer to define the specifics of the project.
Weeks 2-13:
Each small group will work together to advance the project, and will meet with the lecturer (once a week) to discuss their progress and how to move forward .
Week 14:
Presentation of the work to the course participants and lecturers.
Expected projects:
- The projects should be purely computational and should rely only on existing data.
- The projects should be feasible within the timeframe and considering the limited computational experience of the students.
- The project should include common elements of data analysis that are likely to be useful for other computational projects.
Learning Outcomes
Upon successful completion of the course, the students will be able to:
Acquire elements of data analysis that are likely to be useful for other computational projects.